ગ્રોક 4.6

SpaceXAI's frontier model, built for coding, agentic tasks and knowledge work. It builds on Grok 4.5 with a particular focus on long-running agents and more ambitious interactive and visual work, and the lab calls it the most intelligent and fastest model it has built. It is strongest at sustaining complex multi-step projects and at turning broad product concepts into working prototypes; reasoning is always on.

xAI attributes the step up to more work after pre-training than 4.5 got: synthetic material the lab curated itself around reasoning and hard technical ground, engineering data it rates highly, and a reworked optimiser and recipe. It says that combination left a stronger base for the supervised and reinforcement stages that follow. Grok 4.5 was then put to work regenerating the supervised training traces itself - across every effort setting, several agent scaffolds, and subjects from science to software to office work - with a model-run filter discarding the traces it judged unsound. The reinforcement learning stage covered agentic tasks in knowledge work, general coding, and purpose-built environments for kernel optimisation, web development and computer-aided design.

What xAI says that produced is a model that holds a goal across many steps. Its own testing focused on projects chosen to stretch range and endurance, and the lab reports its strongest showing when a vague product brief has to become something that runs: reading up on a domain it does not know, deciding the shape of the application, building the interactions that matter, then tightening them over repeated passes. On longer runs xAI reports the model increasingly stopping to check itself, confirming what it has done before carrying on - which is the behaviour that decides whether an unattended agent finishes or quietly drifts.

The visual side is where the lab draws the sharpest line against the previous generation. xAI reports stronger first passes on visual and interactive projects than it typically saw from Grok 4.5, with the model able to establish an application's structure and visual language in a single pass from a concrete product idea. The argument is about workflow rather than polish: when the fastest route to a good result is to begin with something substantial and then iterate in the loop, a strong first pass is worth more than a clean sketch.

On its own evaluation table, run at high effort, xAI reports Grok 4.6 at 69.9% on CursorBench v3.2 against 66.7% for Grok 4.5, 65.9% on DeepSWE v1.1 against 54%, 61.3% on the extended FrontierCode v1.1 against 56.6%, 57.5% on APEX-Agents against 47.1%, and 56.4% on APEX-SWE. Every one of those is a clear generational gain over the model it replaces.

The same table shows where it does not lead. On Terminal-Bench v3.0 xAI reports 26% against 34.6% and 34.1% for the two frontier models it compares itself with, and its DeepSWE v1.1 result sits below both of them as well. Reasoning cannot be switched off here - the dial runs low, medium, high and xhigh, with high as the default - so there is no cheap non-thinking mode to fall back to on simple calls. The lab says safeguards were recalibrated to the model's wider capability surface, with its broadest pre-deployment test suite to date plus post-deployment and third-party testing, and that the stack is tuned to keep the model usable for vulnerability patching, engineering design work and AI research rather than refusing those outright.